code: def forward(self, x, rel_pos_bias=None):
      h = self.heads
  
      # prenorm
  
      x = self.norm(x)
  
      q, k, v = self.to_qkv(x).chunk(3, dim=-1)
  
      # split heads
  
      q, k, v = map(
          lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v)
      )
      q = q * self.scale
  
      sim = einsum("b h i d, b h j d -> b h i j", q, k)
  
      # add relative positional bias for local tokens
  
      if exists(rel_pos_bias):
          sim = sim + rel_pos_bias
  
      attn = sim.softmax(dim=-1)
      attn = self.dropout(attn)
  
      # merge heads
  
      out = einsum("b h i j, b h j d -> b h i d", attn, v)
      out = rearrange(out, "b h n d -> b n (h d)")
      return self.to_out(out)
  
imports: import torch
  from torch import nn, einsum
  from einops import rearrange
  from einops.layers.torch import Rearrange, Reduce
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, rel_pos_bias) and returns: self